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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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RAG Architecture Explained: Retrieval-Augmented Generation
RAG gives a language model the right pages to read before it answers. This guide walks through the four stages with a pipeline that runs on a laptop, using open models and public documents, and shows three outcomes: a correct cited answer, a retrieval miss the model admits to, and a question outside the documents.
Data Warehouse Explained: Concepts and Architecture
A clear explanation of data warehouses: why analytical queries need a separate store from transactional systems, the architecture from sources through staging, modelled layers and a semantic model, facts, dimensions, grain and history, modelling approaches, modern platforms and common pitfalls.
Medallion Architecture: Bronze, Silver and Gold Layers
The medallion architecture explained by building it: bronze as raw data exactly as it arrived, silver as validated and enriched rows, a quarantine table that keeps every rejected row with its reason, gold shaped for one dashboard, a reconciliation that bronze equals silver plus quarantine, and the mistakes teams make.
Production ML Systems: Architecture and Best Practices
In a production machine learning system the model is the smallest part. This guide maps the components around it, compares the three serving patterns, explains the ML-specific technical debt that architecture has to contain, and sets out the practices that keep a system maintainable as it grows.
Lambda vs Kappa Architecture Explained
Lambda and Kappa architectures compared: Lambda's batch, speed and serving layers, Kappa's single replayable stream, a simulation on a week of real taxi trips in which Lambda's two code paths disagree by 982 trips until the nightly batch runs, how Kappa recomputes by replaying the log, and where lakehouses fit.
Real-Time Analytics: Architecture and Tools
How real-time analytics systems are built: the four layers of ingestion, stream processing, serving and consumption, the tools at each layer including Kafka, Spark, Flink and real-time OLAP databases such as Druid, Pinot and ClickHouse, latency budgets, common architectures, and the pitfalls that raise costs.